未知周期DoS攻击下奇异摄动信息物理系统H∞滤波设计

针对奇异摄动信息物理系统在未知周期拒绝服务攻击(denial-of-service,DoS)下的状态估计问题,提出事件触发多时间尺度切换滤波器设计方法.首先,在传感器与滤波器之间引入事件触发机制确定采样数据是否传输至网络中,可以达到节约网络资源的目的;然后,在此事件触发机制的基础上,考虑未知周期Dos攻击问题,建立滤波误差切换系统,通过构建依赖奇异摄动参数的Lyapunov函数,提出奇异摄动信息物理系统切换滤波器设计方法,得到较小的估计误差,从而克服设计过程中的病态数值问题,降低网络通信中DoS攻击对系统的不良影响,保证误差系统指数稳定且满足H_∞性能,实现未知周期DoS攻击下系统的状态估计;最后,利用电路仿真算例验证所提出的滤波器设计方法的有效性.     

Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multi-sensor remote estimation systems. An optimal stealthy false data injection (FDI) attack scheme based on historical and current residuals, which only tampers with the measurement residuals of partial sensors due to limited attack resources, is proposed to maximally degrade system estimation performance. The attack stealthiness condition is given, and then the estimation error covariance in compromised state is derived to quantify the system performance under attack. The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition. Moreover, due to the constraint of attack resources, the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance. Finally, simulation results are presented to verify the theoretical analysis.      

Indefinite LQ optimal control for stochastic Takagi–Sugeno fuzzy system under sensor data scheduling: Finite-horizon case

The present paper considers the finite-horizon indefinite linear quadratic (LQ) control problem for stochastic Takagi–Sugeno (T-S) fuzzy systems with input delay. In this paper, we consider the presence of sensor data scheduling, which imposes a communication energy constraint and necessitates optimal state estimation for measurements. Then, by utilizing dynamic programming principles, the stochastic LQ problem under consideration can be solved, while the optimal control policy is developed in terms of the unique solutions to a set of coupled difference Riccati equations (CDREs). Specifically, for simple delay-free case, the linear matrix inequalities based conditions are also proposed, whose feasibility is shown to be equivalent to the well-posedness of the indefinite LQ control under consideration. As an application, our theoretic analysis is extended to study the intermittent observation model caused by random denial-of-service attack.     

Adaptive optimisation-offline cyber attack on remote state estimator

Security issues of cyber-physical systems have received increasing attentions in recent years. In this paper, deception attacks on the remote state estimator equipped with the chi-squared failure detector are considered, and it is assumed that the attacker can monitor and modify all the sensor data. A novel adaptive optimisation-offline cyber attack strategy is proposed, where using the current and previous sensor data, the attack can yield the largest estimation error covariance while ensuring to be undetected by the chi-squared monitor. From the attacker's perspective, the attack is better than the existing linear deception attacks to degrade the system performance. Finally, some numerical examples are provided to demonstrate theoretical results.     

Observer‐based attack‐resilient control for linear systems against FDI attacks on communication links from controller to actuators

For the adversarial attacks on the communication links from the controller to the actuators, most of the existing attack‐resilient control results focus on denial‐of‐service attacks. Unlike the existing results, this paper studies the observer‐based attack‐resilient control problem for linear systems with false data injection attacks and process disturbances. Due to limited resources, the malicious attacker is assumed to only manipulate a certain number of communication links from the controller to the actuators. A novel control architecture is proposed, which consists of an attack‐resilient state observer, a controller gain scheme, and a supervisory switching strategy. The observer is developed based on the maximin strategy, and state estimation will be used to construct the controller. The switching strategy is designed to pick an appropriate controller gain and prevent the attack signals from entering the plant automatically. It is shown that the closed‐loop system is stable with an attack‐resilient performance. Finally, to verify the effectiveness of the proposed controller, simulation results on a linearized reduced‐order aircraft system and an IEEE six‐bus power system are provided.     

非线性离散时间系统带ε误差限的自适应动态规划

为了获得非线性离散时间系统的最优控制策略,基于自适应动态规划的原理,提出了一种带误差限的自适应动态规划方法.对于一个任意的状态,用一个有限长度的控制序列近似最优控制序列,使性能指标与最优性能指标的误差在一个较小的范围内.选取一个非线性离散时间系统对算法的性能进行数值实验,结果验证了该算法的有效性,用较少的计算代价获得了近似最优的控制策略.     

轮询通信协议下信息物理系统分布式状态估计

本文关注的是一类信息物理系统的分布式状态估计问题. 由于传感器网络通信带宽有限, 当大量节点同时发送数据时, 可能造成数据冲突. 因此, 通过引入轮询协议减轻传感器网络通信负担, 在该协议下每个节点的测量分量将依次且周期性访问网络. 考虑滤波网络拓扑切换概率矩阵是时变的, 因此采用非齐次Markov链描述随机拓扑切换行为. 证明了估计误差以指数衰减的形式收敛,确保了滤波误差系统在均方意义下最终有界. 进一步地, 通过解决特定拓扑依赖的凸优化问题, 获得期望的分布式滤波器参数. 最后, 通过两个例子证明了所设计的分布式状态估计方法的可行性.     

基于模型预测的PHEV模式切换协调控制

针对混合动力汽车在模式切换过程中,由于离合器的结合/分离易产生较大冲击度和滑磨功,提出一种模型预测切换协调控制方法。在进行动力学分析基础上,建立预测模型。以电机驱动下的速度响应作为目标转速,构造目标函数,考虑其非可行解问题、动力部件物理约束以及最大冲击度限制要求,引入松弛因子,设计状态约束条件,保证其解的可行性与切换平顺性,实现较小冲击度与滑磨功。同时,引入预测误差,对预测值进行反馈校正,以提高控制精度;将所提策略转化为在线二次规划问题,便于计算机实现。仿真验证结果表明,所提协调控制策略与传统策略相比,冲击度与滑磨功明显降低,保证了切换平顺性,提高了驾驶性能。     

State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. The norm-bounded uncertainties enter into the system in a randomly way, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequence with known conditional probability. By constructing a new Lyapunov–Krasovskii functional, sufficient conditions are proposed to guarantee the convergence of the estimation error for all discrete time-varying delays, ROUs and distributed sensor delays. Subsequently, the explicit form of the estimator parameter is derived by solving two linear matrix inequalities (LMIs) which can be easily tested by using standard numerical software. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed estimation scheme.     

Stochastic DoS Attack Allocation Against Collaborative Estimation in Sensor Networks

In this paper, denial of service (DoS) attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is studied. In the communication channels between sensors and a remote estimator, the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped. A stochastic power allocation approach composed of three steps is proposed. Firstly, the minimum number of channels and the channel set to be attacked are given. Secondly, a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems, respectively. Finally, by converting the original coupling nonlinear programming problem to a linear programming problem, a method of searching attack probabilities and power to minimize the attack energy is proposed. The effectiveness of the proposed scheme is verified by simulation examples.      

拒绝服务攻击下基于UKF的智能电网动态状态估计研究

针对连续拒绝服务（Denial of service，DoS）攻击导致量测数据丢失使得动态状态估计失效、进而破坏智能电网安全经济运行问题，本文提出了一种适用拒绝服务攻击的改进无迹卡尔曼滤波（Unscented Kalman filter，UKF）方法，以进行智能电网动态状态估计.首先，分析拒绝服务攻击引起数据丢包特性并设计了数据补偿策略，以重构电力系统动态模型；然后，结合Holt's双参数指数平滑和无迹卡尔曼滤波方法，构造了融合补偿信息的新状态估计方程，并进一步基于估计误差协方差矩阵推导了状态增益更新方法，从而得到了无迹卡尔曼滤波动态状态估计新方法.最后，针对IEEE 30和118节点系统进行仿真，验证了所提方法的可行性和有效性.     

The problem of optimal robust sensor scheduling

This paper considers the sensor scheduling problem which consists of estimating the state of an uncertain process based on measurements obtained by switching a given set of noisy sensors. The noise and uncertainty models considered in this paper are assumed to be unknown deterministic functions which satisfy an energy type constraint known as an integral quadratic constraint. The problem of optimal robust sensor scheduling is formulated and solution to this problem is given in terms of the existence of suitable solutions to a Riccati differential equation of the game type and a dynamic programming equation. Furthermore, a real time implementable method for sensor scheduling is also presented.     

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi-square test-based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi-square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real-time attack detection.     

Path selection with Nash Q‐learning for remote state estimation over multihop relay network

In this article, the security issue of remote state estimation is investigated for multihop relay networks interrupted by an attacker launching denial‐of‐service attacks. Since the presence of the relay enriches the communication topology, there might exist several paths connecting the sensor and the estimator, consisting of the corresponding channels. Thus, it is reasonable for the sensor to select the path with a lower dropout rate to enhance the system performance measured by the estimation error, due to the dropout rate changing with the channel. However, as an adversary, the objective of the jammer is to deteriorate the corresponding performance through launching attack on the communication path selectively. For addressing the problem on the behalf of both of the sensor and the jammer, we first formulate this problem as a two‐player zero‐sum stochastic game model, and then present a Nash Q‐learning algorithm to explore the equilibrium point for both players, under the assumption that both of them are rational players. Furthermore, the existence of equilibrium point for this problem is proved analytically. Moreover, a more general case of the channel attack, under which the jammer can attack any channels among this network, is considered. Finally, numerical results are presented to verify the effectiveness of the algorithm proposed and the theorem results.     

Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

In this paper, a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems. The system state is forced to track the reference signal by minimizing the performance function. First, the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function (also named as action value function). Then, an iterative algorithm based on adaptive dynamic programming (ADP) is developed to find the optimal solution which is totally based on sampled data. The linear-in-parameter (LIP) neural network is taken as the value function approximator. Considering the presence of approximation error at each iteration step, the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions. Moreover, the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper. A sufficient condition for asymptotically stability of the tracking error is derived. Finally, the effectiveness of the algorithm is demonstrated with three simulation examples.      

Stability and performance of switching Kalman filters

This paper considers the state estimation problem for switched affine systems. A switching Kalman filter is proposed and its performance analysed. It is proved that this filter leads to an optimal and stable estimation for all switching sequences of the system. In contrast to this, a switching stationary filter may cause an infinitely growing estimation error, which is demonstrated by an example.     

ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.     

An efficient optimal solution to the coil sequencing problem in electro-galvanizing line

This paper studies a coil sequencing problem that arises from electro-galvanizing line in steel industry. The problem is to find a processing sequence of steel coils such that the switching costs between consecutive coils are minimized while satisfying technical restrictions. The problem can be decomposed into several independent sub-problems, each corresponding to a turn which is a sequence of continuously processed coils with the same post-processing requirement. The coils in each turn can be further divided into several families each consisting of the coils with the same width. Based on analysis of the problem properties, a two-phase polynomial algorithm is developed to obtain an optimal turn. The sequence of coils in a family with given boundary coils (first and last coils) is determined in the first phase using a polynomial dynamic programming algorithm. In the second phase, the optimal turn is formed by another polynomial dynamic programming algorithm which takes the boundary coils for each family as state variables. The efficiency of the proposed algorithm is demonstrated through computational experiments.     

一种能量有效的WSN目标跟踪动态协同自组织算法

针对无线传感器网络目标跟踪应用中跟踪精度与网络能耗的权衡问题,提出一种能量有效的动态协同自组织算法(E-DCS)。根据目标预测位置和节点的位置、能量信息,建立了信息效用、通信开销和节点剩余能量的综合性能指标,并利用层次分析法确定了性能指标中各要素的权值系数。通过自适应动态成簇策略,分别设定簇首切换精度阈值和节点选择精度阈值判断是否切换簇首和选择任务节点。簇首节点根据簇内节点提供的测量信息采用序贯EKF进行状态估计。仿真结果表明,与信息驱动传感器查询(IDSQ)和自适应动态协同自组织算法(A-DCS)相比,该算法在保证跟踪精度的基础上,降低了网络能耗,有效延长了网络的生命周期。